Preventing Brain Injury in Newborns With Congenital Heart Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Newborns with congenital heart disease are at high risk for brain injury and adverse neurodevelopmental outcomes. MRI enables the objective determination of the severity of brain injury in critically ill newborns with congenital heart disease. We will rationalize the use of MRI as a surrogate for neurodevelopmental outcome and describe novel randomization techniques that can be used in trials in this population. METHODS: This article describes the evidence for the use of MRI and the link with neurodevelopmental outcome established in newborns. We also discuss the use of adaptive randomization techniques for future clinical trials in newborns with congenital heart disease. These strategies will be highlighted using an example. RESULTS: Brain injuries occur with high frequency in newborns with congenital heart disease. It is not until school age that the full extent of neurological sequelae becomes apparent and the rapid pace of innovation in neonatal cardiac surgery prevents timely evaluation of changes in care. MRI provides a timely, safe, and reliable outcome measure and has been extensively studied in newborns with other conditions in which the link between brain injury and neurodevelopmental outcome has been established. Clinical trials using MRI as an outcome measure as well as adaptive randomization can improve the efficiency of such trials. CONCLUSIONS: Clinical trials of brain protection are urgently needed in newborns with congenital heart disease given the unacceptable frequency of brain injury in this population; MRI provides an early surrogate marker of long-term neurodevelopmental outcome and adaptive randomization can be used to improve the efficiency of these clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".